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Cooperative Perception and Planning for Swarm Robot Systems
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Cooperative Perception and Planning for Swarm Robot Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 29 August 2024 | Viewed by 2015

Special Issue Editors

Prof. Dr. Heoncheol Lee

E-Mail Website
Guest Editor
Department of IT Convergence Engineering, School of Electronic Engineering, Kumoh National Institute of Technology, Gyeongbuk 39177, Republic of Korea
Interests: SLAM; autonomous navigation; multi-robot systems; deep learning for anomaly detection; FPGA-based algorithm acceleration
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Seunghwan Lee

E-Mail Website
Guest Editor
School of Electronic Engineering, Kumoh National Institute of Technology, Gyeongbuk 39177, Republic of Korea
Interests: multiagent systems; autonomous navigation; SLAM
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the field of robotics, perception and planning algorithms and frameworks have been in development for decades. Recently, swarm robot systems have received attention because they have the potential to solve the existing and challenging problems, even where it would otherwise be difficult to do so with one or multiple robots. However, in order to implement the swarm robot systems, it is necessary to develop cooperative perception and planning algorithms and frameworks. This will be highly challenging due to the errors in not only robot-to-environment measurements but also in robot-to-robot measurements. Moreover, the possibility of causing collisions increases due to the increased number of robots. If the robots in the swarm are operating in different or unknown coordinate systems, the cooperation among robots becomes more challenging; instead, researchers should firstly find the common coordinate system before conducting their missions.

This Special Issue aims to provide a broad coverage of recent trends and advances in cooperative perception and planning algorithms and frameworks for swarm robot systems. Researchers are welcome to submit both theoretical and practical works, as well as review/survey papers in the area. The topics of interest of this Special Issue include, but are not limited to:

  • Cooperative perception frameworks for swarm robot systems;
  • Cooperative perception algorithms for swarm robot systems;
  • Cooperative planning frameworks for swarm robot systems;
  • Cooperative planning algorithms for swarm robot systems;
  • Algorithms and implementation for multiple map merging;
  • Algorithms and implementation for multi-robot cooperation;
  • Practical sensor fusion systems for inter-robot recognition;
  • Efficient inter-robot collision avoidance;
  • Efficient inter-robot communication;
  • Real-time systems with cooperative perception and planning algorithms;
  • Edge device techniques to implement swarm robot systems;
  • Algorithm parallelization and acceleration for embedded robotics;
  • Heterogeneous processing systems for embedded robotics.

Prof. Dr. Heoncheol Lee
Prof. Dr. Seunghwan Lee
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • swarm robot systems
  • multi-robot systems
  • cooperative perception, cooperative planning
  • cooperative navigation
  • multi-robot cooperation
  • inter-robot recognition, inter-robot collision avoidance, inter-robot communication
  • real-time systems
  • algorithm parallelization and acceleration
  • embedded robotics

Published Papers (2 papers)

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19 pages, 5594 KiB  
Article
Hierarchical Area-Based and Path-Based Heuristic Approaches for Multirobot Coverage Path Planning with Performance Analysis in Surveillance Systems
by Junghwan Gong and Seunghwan Lee
Sensors 2023, 23(20), 8533; https://doi.org/10.3390/s23208533 - 17 Oct 2023
Cited by 1 | Viewed by 695
Abstract
In this study, we present a systematic exploration of hierarchical designs for multirobot coverage path planning (MCPP) with a special focus on surveillance applications. Unlike conventional studies centered on cleaning tasks, our investigation delves into the realm of surveillance problems, specifically incorporating the [...] Read more.
In this study, we present a systematic exploration of hierarchical designs for multirobot coverage path planning (MCPP) with a special focus on surveillance applications. Unlike conventional studies centered on cleaning tasks, our investigation delves into the realm of surveillance problems, specifically incorporating the sensing range (SR) factor equipped on the robots. Conventional path-based MCPP strategies considering SR, primarily rely on naive approaches, generating nodes (viewpoints) to be visited and a global path based on these nodes. Therefore, our study proposes a general MCPP framework for surveillance by dealing with path-based and area-based structures comprehensively. As the traveling salesman problem (TSP) solvers, our approach incorporates not the naive approach but renowned and powerful algorithms such as genetic algorithms (GAs), and ant colony optimization (ACO) to enhance the planning process. We devise six distinct methods within the proposed MCPP framework. Two methods adopt area-based approaches which segments areas via a hierarchical max-flow routing algorithm based on SR and the number of robots. TSP challenges within each area are tackled using a GA or ACO, and the result paths are allocated to individual robots. The remaining four methods are categorized by the path-based approaches with global–local structures such as GA-GA, GA-ACO, ACO-GA, and ACO-ACO. Unlike conventional methods which requires a global path, we further incorporate ACO- or GA-based local planning. This supplementary step at the local level enhances the quality of the path-planning results, particularly when dealing with a large number of nodes, by preventing any degradation in global path-planning outcomes. An extensive comparative analysis is conducted to evaluate the proposed framework based on execution time, total path length, and idle time. The area-based approaches tend to show a better execution time and overall path length performance compared to the path-based approaches. However, the path-based MCPP methods have the advantage of having a smaller idle time than the area-based MCPP methods. Our study finds that the proposed area-based MCPP method excels in path planning, while the proposed path-based MCPP method demonstrates superior coverage balance performance. By selecting an appropriate MCPP structure based on the specific application requirements, leveraging the strengths of both methodologies, efficient MCPP execution becomes attainable. Looking forward, our future work will focus on tailoring these MCPP structures to diverse real-world conditions, aiming to propose the most suitable approach for specific applications. Full article
(This article belongs to the Special Issue Cooperative Perception and Planning for Swarm Robot Systems)
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23 pages, 3256 KiB  
Article
Parallelized Particle Swarm Optimization on FPGA for Realtime Ballistic Target Tracking
by Juhyeon Park, Heoncheol Lee, Hyuck-Hoon Kwon, Yeji Hwang and Wonseok Choi
Sensors 2023, 23(20), 8456; https://doi.org/10.3390/s23208456 - 13 Oct 2023
Cited by 1 | Viewed by 878
Abstract
This paper addresses the problem of tracking a high-speed ballistic target in real time. Particle swarm optimization (PSO) can be a solution to overcome the motion of the ballistic target and the nonlinearity of the measurement model. However, in general, particle swarm optimization [...] Read more.
This paper addresses the problem of tracking a high-speed ballistic target in real time. Particle swarm optimization (PSO) can be a solution to overcome the motion of the ballistic target and the nonlinearity of the measurement model. However, in general, particle swarm optimization requires a great deal of computation time, so it is difficult to apply to realtime systems. In this paper, we propose a parallelized particle swarm optimization technique using field-programmable gate array (FPGA) to be accelerated for realtime ballistic target tracking. The realtime performance of the proposed method has been tested and analyzed on a well-known heterogeneous processing system with a field-programmable gate array. The proposed parallelized particle swarm optimization was successfully conducted on the heterogeneous processing system and produced similar tracking results. Also, compared to conventional particle swarm optimization, which is based on the only central processing unit, the computation time is significantly reduced by up to 3.89×. Full article
(This article belongs to the Special Issue Cooperative Perception and Planning for Swarm Robot Systems)
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